Boom, B.J. (Bas)

Fisher, R.B. (Robert)

He, J. (Jiyin)

Huang, X.

English

CWI's Institutional Repository

Supporting Ground-Truth annotation of image datasets using clusteringBastiaan J. Boom1, Phoenix X. Huang1, Jiyin He2, Robert B. Fisher11School of Informatics, University of Edinburgh2Interactive Information Access, CWI, AmsterdamAbstractAs more subject-specific image datasets (medical im-ages, birds, etc) become available, high quality labelsassociated with these datasets are essential for buildingstatistical models and method evaluation. Obtainingthese annotations is a time-comsuming and thus a costlybusiness. We propose a clustering method to supportthis annotation task, making the task easier and moreefficient to perform for users. In this paper, we pro-vide a framework to illustrate how a clustering methodcan support the annotation task. A large reduction inboth the time to annotate images and number of mouseclicks needed for the annotation is achieved. By in-vestigating the quality of the annotation, we show thatthis framework is affected by the particular clusteringmethod used. This, however, does not have a large in-fluence on the overall accuracy and disappears if thedata is annotated by multiple persons.1. IntroductionOne of the most common problems given a newlyacquired dataset is to attach labels to this dataset and of-ten (especially in medical imaging) experts are neededto determine these labels. A similar problem is howto obtain groundtruth classifications (e.g. fish species)for a large dataset of images (e.g. underwater imagesof fish), where to guarantee the quality we would likea certain number of users to annotate the images. Bysupporting this task with a clustering method we solvedtwo problems at the same time: Firstly, by translatingthe task from recognizing fish species to cluster valida-tion, the expert knowledge needed is greatly reduced.Secondly, by clustering the images using computer vi-sion features, the annotation process is more efficient.Previous approaches for the annotation of a set of im-ages are, for instance, the ESP Game [7] and LabelMe[6]. Most of this work is focused on a large variety ofinternet images where often multiple tags can be givento these images. These tools are useful in the case ofrandom internet images, but are not efficient for solv-ing annotation problems where we want to obtain a sin-gle specific label for an image. Recent work more suit-able to this problem involves the annotation of a birddatabase, where users label certain properties of a birdlike the color of tail, wings, beak [9]. This focuses onsubject-specific image datasets, however it might not bea very efficient way of annotating images as multipleproperties have to be assigned to each image.Alternatively, there are approaches which combineuser annotations and machine learning to obtain thegroundtruth labels, for example, [5], but this does notspeed up the annotation task. Another approach thatrelies less on the automatic methods and allows usersto search and annotate images at the same time is [8].These approaches are developed for internet images onthe web and need all labels to be defined apriori.In our approach, we want to annotate all the images inthe dataset, where we focus on subject-specific datasets.This allows the use of specific domain dependent fea-tures to cluster these datasets, which can support theannotation by users or experts. This has not been at-tempted before to our knowledge. Our approach ex-plores both the Kullback-Liebler divergence [3] andPyramid histogram [1] for the clustering of automati-cally segmented fish images (Section 2.1) after whichindividual annotators refine and group clusters usingtwo specialized interfaces (Section 2.2) and then theresult of multiple users are combined in (Section 2.3).Experiments show a reduction of up to 77% of annota-tion time and 93% of mouse clicks while maintain ac-curacy(Section 3).2. Ground-truth annotation using auto-matic clustering2.1. Fish Clustering MethodsTo cluster the fish images, two methods for obtain-ing features and calculating a distance measure between  post clustering results: green are cluster representativesyellow are rest of examples in clusterstage 1: remove examples with different label from the clustersstage 2: link representativesof clusters to labelsstage 3: link removed examples from clusters to labelsFigure 1: A schematic representation of theframework for annotating images with the sup-port of a clustering methodfish images were used: The first method [3] computesthe Kullback-Liebler divergence (KL divergence) be-tween feature sets. For the fish, we create sets ofcolor, texture and contour features (where we respec-tively used the Hue/Saturation/Value, the Canny edgedetector and the Curvature Scale Space representation).A Gaussian Mixture Model (GMM) is estimated fromthe set of features of a fish (for instance the color) andKL divergence is computed between two GMMs. Thesecond method [1] uses a pyramid histogram of visualwords (dense SIFT features with color information).These histograms describing each fish are normalizedand the Euclidean distance between the histograms arecomputed. To compute clusters based on these distancemeasures, we use Affinity Propagation [2] which alsoprovides a representative image for each cluster. Therepresentative image is important because we can rep-resent a cluster by a single image.2.2. A Cluster-based Annotation FrameworkManual annotation of thousands of images for thetask of recognition can be time consuming. Efficiencyis improved by using a clustering method. Instead ofgiving a label for every fish, the user verifies that a fishimage is similar to another fish image. Thus the taskof the user changes from entering fish names to judgingthe estimated similarity between images. Although thistask can still be difficult, it does not require as much do-main knowledge as the previous task.The framework to label an entire dataset of images us-ing a clustering method consists of three stages (Fig-ure 1 shows a schematic of this framework):(a) The first interface to remove images from the cluster by clickingon the image that does not belong to the same label as the repre-sentative image in the top row(b) The second interface to link the image in the top row to a labelby clicking on one of the gallery images which belonging to thesame label or add a new label by pressing the green plus buttonFigure 2: Interfaces1. Cleaning the clusters (blue ovals in Figure 1),where we remove images which are not similar tothe representative image (green square).2. Merging the clusters, using the representative im-ages of the cleaned clusters to link them to labels(shown as purple diamonds)3. Linking removed images (shown as red squares)from the cleaning stage to the labels.In this paper, we use the definition “cluster” for a groupof images which are similar as determined by an auto-matic algorithm. The definition for “label” is a group ofimages which belong to the same category from the per-spective of the human annotator and this group containsall the images in this category. In the case of fish, thismeans that a label includes all fish of a certain speciesin the dataset. Note that the particular species name isnot necessary at this stage and can be added afterwardsto the labels by a domain expert.For the first stage, we use the cleaning interface shownin Figure 2(a). In this case, the representative clusterimage is shown at the top of the screen and the rest ofthe images in that cluster are put under this image. Theuser only has to select the images which are not cor-rectly clustered and continue to the next window. Aftercleaning all the clusters, there are basically three kindsof images in the dataset: 1) The representative clusterimages, 2) the images that belong to clusters and 3) im-ages that are not part of a cluster.In the second stage, users link the clusters to labels us-ing the representative images. This is because, for im-proved cluster coherence, we overcluster (e.g. 156 clus-ters for 32 labels) and therefore need to merge clusters.Notice that by linking these images, we also immedi-ately link the images that belong to the underlining clus-ters. The second interface shown in Figure 2(b) is usedto link the representative image either to one of the pre-vious representative images of a label or the user willcreate a new label by pressing the green plus button. Inthe first case, the cluster is categorized under the samelabel. In the second case, a new label and representativelabel image are created.In the third stage, we link the set of images that are notpart of a cluster, using the same interface as in the pre-vious step to also link these images to a label. In thiswork, the final goal is to label an entire dataset which iscomparable with the normal labeling task of annotatingeach image individually. It is however possible to skipstage 3 or in case of a very large datasets, it is possi-ble to recluster the images which are removed from theclusters in stage 1, which may speedup the annotationeven more.2.3. Combining Multiple AnnotatorsThe problem of combining the annotations frommultiple users is discussed in [10] and [4]. In the frame-work describe by [10], we have an observed label Lijfor each image j of the M images given by each useri of the N users. The expertise (accuracy in annota-tion) of user i is modeled by the parameter αi and thedifficulty of the image j is given by the parameter βj .The groundtruth image label is denoted by Zj . In [10],Expectation-Maximization is used to infer both αi, βjand Zj given the observed labels Lij . In [4], the la-bels from an expert are used to estimate αi and βj ona small number of images that this expert also anno-tated, from which we can compute βj and Zj on theremaining images. We extended the work of [4] fromtwo classes to support multiple classes, which allows usto find groundtruth labels for all images.3. ExperimentWe empirically investigate our proposed frameworkwith a dataset of 3678 automatically segmented fishimages obtained from underwater surveillance cameraswith 32 different fish species in the dataset. The datasetis annotated by 6 users using the KL divergence andby 2 of the 6 users again using the Pyramid histogram.A part of the dataset (159 images) is also labeled bymarine biologists, allowing us to obtain the groundtruth(using [4]) by combining all the annotations.Figure 3(a) shows the accuracy at each of the stages.Because the annotation in the first stage depends onthe clustering performance, this stage is divided intotwo boxplots. It is clear from these boxplots that usersmake more mistakes with removing the incorrectly clus-tered images than with correctly clustered images. Weassume that this has two causes: The first cause isthat users do not scan the images very comprehen-sively, which leads to labeling mistakes which couldbe avoided. The second cause is that some images arehard to recognize and users might not be able to sep-arate them correctly. The performance in stage 2 (seeFigure 3(a)) is a good indication of the labeling perfor-mance without using clustering, because stage 2 has theuser select a pictorial “label” for each presented image.In our case, we only present the representative imagesrather than the full set, but we argue that accuracy wouldbe similar if all images were presented. From the per-formance of stage 3, we observe that it is also more dif-ficult to link the images excluded from stage 1 (whichwere incorrectly clustered), than linking the representa-tive images.Overall quality: In order to measure the performanceof multiple users annotating the dataset, we calculate allsubsets of users and combine their annotations for thesix users who labeled with KL divergence. Figure 3(b)gives the average performance in annotation for com-bining a certain number of users. By comparing the“Overall” results, which shows the accuracy of anno-tation with clustering, to the “Stage 2” results, whichestimates the accuracy of annotating all images with-out clustering, there is in most cases a small decreasein accuracy due to the clustering. The first 2 bins inFigure 3(b) show the difference between the user per-formance on the correctly clustered images and incor-rectly clustered images as discussed before. The incor-rectly clustered images have only a small influence onthe overall performance, because the percentage of in-correctly clustered images for KL divergence and Pyra-mid histograms is respectively 9.8% and 16.9%.Gain in time and mouse clicks: To estimate the time ittakes to annotate the images, one of our users performedthe labeling non-stop, allowing us to measure the aver-age time it takes to finish one screen. In the case ofthe first interface, it took an average time of 19.7 sec-onds to complete one screen and for the second inter-face it took an average time of 7.3 seconds. Figure 3(c)5060708090100PercentageofcorrectlylabeledimagesStage 1:ClusteringCorrectStage 1:ClusteringIncorrectStage 2 Stage 3(a) Box plot of the performance in labeling ofall users for the individual stages1 2 3 4 5 66065707580859095100Number of usersAveragepercentageofcorrectlylabeledimagesClustering correctClustering incorrectOverallStage 2(b) Histogram of the average combined perfor-mance of a certain number of userstime mouse clicks05101520253035PercentagecomparedtoannotatingallimagesKL divergencePyramid histogram(c) The improvement in both time and mouseclicks over annotating all imagesFigure 3: Evaluationshows the improvement in time based on extrapolationof these values for all users and both clustering methodsin comparison to labeling all images using the secondinterface. The number of mouse clicks is important incrowdsourcing, because users often get paid per click.If one labeled all the images using the second interface,we would need 2M clicks to select and confirm the cor-rect species (stage 2 interface). In the first interface, weonly click on images that have to be removed from thecluster and need an extra click to confirm our annotationfor each cluster. In the second stage we click twice toselect and confirm the label for only the representativeimages and all the images excluded from stage 1. Thenet results is about a 77% reduction in label time and93% reduction in mouse clicks when using KL diver-gence.4. ConclusionAn efficient framework to annotate images ispresented in this paper. The quality of this annotationframework is affected by the clustering method (5.1%error by combining 3 users), however it does notseem to affect the quality of the annotations too muchcompared to the estimated quality of labeling all theimages in the dataset without clustering (4.2% errorby combining 3 users). This difference in qualitygets smaller if more users are annotating. With theclusters based framework, we can label the dataset threetimes in the time it takes to label all images withoutclustering, which also gives a better quality of labels.This framework has also been used without stage 3 tolabel a dataset of around 23000 fish images, which tookabout 8 hours for each of the three users annotating.Acknowledgements: This work is supported bythe Fish4Knowledge project, which is funded bythe European Union 7th Framework Programme[FP7/2007-2013].References[1] A. Bosch, A. Zisserman, and X. Mun˜oz. Image classi-fication using random forests and ferns. In IEEE 11thInt Conf on Computer Vision, 2007. ICCV 2007, pages1–8, Oct. 2007.[2] B. J. Frey and D. Dueck. Clustering by passing mes-sages between data points. Science, 315(5814):972 –976, Feb. 2007.[3] J. Goldberger, S. Gordon, and H. Greenspan. Unsu-pervised image-set clustering using an information the-oretic framework. IEEE Trans. on Image Processing,15(2):449–458, Feb. 2006.[4] F. K. Khattak and A. Salleb-Aouissi. Quality controlof crowd labeling through expert evaluation. In Sec-ond Workshop on Computational Social Science and theWisdom of Crowds (NIPS 2011), pages 1–5, Dec 2011.[5] V. C. Raykar, S. Yu, L. H. Zhao, G. H. Valadez,C. Florin, L. Bogoni, and L. Moy. Learning fromcrowds. J. Mach. Learn. Res., 99:1297–1322, 2010.[6] B. Russell, A. Torralba, K. Murphy, and W. Freeman.Labelme: A database and web-based tool for image an-notation. Int J of Computer Vision, 77:157–173, 2008.[7] L. von Ahn and L. Dabbish. Labeling images with acomputer game. In Proc of the SIGCHI conf on Humanfactors in computing systems, CHI ’04, pages 319–326,2004.[8] X.-J. Wang, L. Zhang, F. Jing, and W.-Y. Ma. An-nosearch: Image auto-annotation by search. In CVPR2006, volume 2, pages 1483 – 1490, 2006.[9] P. Welinder and P. Perona. Online crowdsourcing: Rat-ing annotators and obtaining cost-effective labels. InCVPR Workshops 2010, pages 25 –32, june 2010.[10] J. Whitehill, P. Ruvolo, T. Wu, J. Bergsma, and J. R.Movellan. Whose vote should count more: Optimal in-tegration of labels from labelers of unknown expertise.In NIPS, pages 2035–2043, 2009.

Supporting Ground-Truth annotation of image datasets using clustering

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